# BANER: Boundary-Aware LLMs for Few-Shot Named Entity Recognition

arXiv: https://arxiv.org/abs/2412.02228 · HTML: https://mlsanity.com/paper/2412.02228/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 42 references that are in the map.
- First posted on arXiv 3 Dec 2024.

## Builds on (most cited first, 27 of 27)

- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- [LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA Composition](https://mlsanity.com/paper/2307.13269/) (2023)
- [End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF](https://mlsanity.com/paper/1603.01354/) (2016)
- [Named Entity Recognition with Bidirectional LSTM-CNNs](https://mlsanity.com/paper/1511.08308/) (2015)
- [Template-Based Named Entity Recognition Using BART](https://mlsanity.com/paper/2106.01760/) (2021)
- [Few-NERD: A Few-Shot Named Entity Recognition Dataset](https://mlsanity.com/paper/2105.07464/) (2021)
- [Large Language Model Is Not a Good Few-shot Information Extractor, but a Good Reranker for Hard Samples!](https://mlsanity.com/paper/2303.08559/) (2023)
- [Template-free Prompt Tuning for Few-shot NER](https://mlsanity.com/paper/2109.13532/) (2021)
- [Few-shot classification in Named Entity Recognition Task](https://mlsanity.com/paper/1812.06158/) (2018)
- [Adapt or Get Left Behind: Domain Adaptation through BERT Language Model Finetuning for Aspect-Target Sentiment Classification](https://mlsanity.com/paper/1908.11860/) (2019)
- [Locate and Label: A Two-stage Identifier for Nested Named Entity Recognition](https://mlsanity.com/paper/2105.06804/) (2021)
- [Empirical Study of Zero-Shot NER with ChatGPT](https://mlsanity.com/paper/2310.10035/) (2023)
- [Good Examples Make A Faster Learner: Simple Demonstration-based Learning for Low-resource NER](https://mlsanity.com/paper/2110.08454/) (2021)
- [Efficient Domain Adaptation of Language Models via Adaptive Tokenization](https://mlsanity.com/paper/2109.07460/) (2021)
- [Label-Agnostic Sequence Labeling by Copying Nearest Neighbors](https://mlsanity.com/paper/1906.04225/) (2019)
- [An Enhanced Span-based Decomposition Method for Few-Shot Sequence Labeling](https://mlsanity.com/paper/2109.13023/) (2021)
- [Few-shot Named Entity Recognition with Entity-level Prototypical Network Enhanced by Dispersedly Distributed Prototypes](https://mlsanity.com/paper/2208.08023/) (2022)
- [Learning from Language Description: Low-shot Named Entity Recognition via Decomposed Framework](https://mlsanity.com/paper/2109.05357/) (2021)
- [Semi-supervised Models are Strong Unsupervised Domain Adaptation Learners](https://mlsanity.com/paper/2106.00417/) (2021)
- [Question-guided Knowledge Graph Re-scoring and Injection for Knowledge Graph Question Answering](https://mlsanity.com/paper/2410.01401/) (2024)
- [LinkNER: Linking Local Named Entity Recognition Models to Large Language Models using Uncertainty](https://mlsanity.com/paper/2402.10573/) (2024)
- [Localising In-Domain Adaptation of Transformer-Based Biomedical Language Models](https://mlsanity.com/paper/2212.10422/) (2022)
- [BERTweetFR : Domain Adaptation of Pre-Trained Language Models for French Tweets](https://mlsanity.com/paper/2109.10234/) (2021)
- [FCDS: Fusing Constituency and Dependency Syntax into Document-Level Relation Extraction](https://mlsanity.com/paper/2403.01886/) (2024)
- [Robust Few-Shot Named Entity Recognition with Boundary Discrimination and Correlation Purification](https://mlsanity.com/paper/2312.07961/) (2023)

## Built on by (most cited first, 3 of 3)

- [Evaluating and Steering Modality Preferences in Multimodal Large Language Model](https://mlsanity.com/paper/2505.20977/) (2025)
- [Bridging Generative and Discriminative Learning: Few-Shot Relation Extraction via Two-Stage Knowledge-Guided Pre-training](https://mlsanity.com/paper/2505.12236/) (2025)
- [SAMA: Semantic Anchor-aligned Augmentation for Unified Low-Resource Multimodal Information Extraction](https://mlsanity.com/paper/2606.18780/) (2026)

## Abstract (arXiv)

Despite the recent success of two-stage prototypical networks in few-shot named entity recognition (NER), challenges such as over/under-detected false spans in the span detection stage and unaligned entity prototypes in the type classification stage persist. Additionally, LLMs have not proven to be effective few-shot information extractors in general. In this paper, we propose an approach called Boundary-Aware LLMs for Few-Shot Named Entity Recognition to address these issues. We introduce a boundary-aware contrastive learning strategy to enhance the LLM's ability to perceive entity boundaries for generalized entity spans. Additionally, we utilize LoRAHub to align information from the target domain to the source domain, thereby enhancing adaptive cross-domain classification capabilities. Extensive experiments across various benchmarks demonstrate that our framework outperforms prior methods, validating its effectiveness. In particular, the proposed strategies demonstrate effectiveness across a range of LLM architectures. The code and data are released on https://github.com/UESTC-GQJ/BANER.